1. Introduction

In this course you will be designing and implementing your own research study.

Over the course of the next 13 weeks we are going to learn about the many different skills needed to do such a study.

However, before we start, it is important for you to have a general sense of what the overall research process looks like. It is important to see the whole, before we start getting into the details of all the parts.

This lecture focuses on giving you a sense of the whole. An overview of a research study.

2. Stages of a study

We will think about a research study as having four main stages:

planning,

data collection,

data analysis, and

writing.

While you could think of this as a linear process - a start, middle, and end - most researchers tend to think of this as a cyclical process.

We start with planning, then collect data, analyse it, and the write it up, but at the end of this process, what we write will become the starting point for future research, and so the cycle begins again.

3. Scope

In the planning stage, the most important task is to define the scope of the project. The scope is the extent or area or ambition of the project - what does it seek to study, how does it seek to study it, what does it seek to conclude? The main problem for researchers is to try to balance two priorities:

We want to do something that is important, and meaningful. So we want speak to important, big issues, and make a contribution. This priority pushes us towards being overly ambitious and grandiose.

We only have limited time, energy, and resources. This priority pushes us towards limiting our ambitions and narrowing our horizons.

There is no magic formula for balancing these priorities. It comes down to trying to answer many unanswerable questions, including:

What is the most important way to contribute to current knowledge?

How much can I do with the resources I have?

What do I think I will find?

How sure am I that I will find this?

3.1 “Speak to big issues, with a tight budget.”

Generally, however, a good rule of thumb is to try to:

Be ambitious in trying to speak to important topics and problems. Try to contribute to big debates and important ideas, things that matter.

Be modest in what you expect you can contribute. You will ultimately, generally, only get to make one point - which can be said in one sentence - with any study. Focus your energies on testing/demonstrating that one point well. And be modest with your plans for data collection, data analysis, and output.

I guess you could summarise this as: “Speak to big issues, with a tight budget.”

The budget, in the case of most research, is not actual money (at least not for undergraduate or graduate students), but rather your budget is time and energy. When you narrow and limit your topic, you reduce the amount of literature you need to review, you reduce the work you need to do preparing ethics applications, and collecting data, and analysing that data. And when you design focused, small budget projects, you often help yourself by simply forcing you to really be clear about your priorities.

Example of a study’s scope

We can see a good example of speaking to big issues, with a tight budget in the “Hairrifying Truth” presentation by my former students in Singapore.

Big issues: Notice how they speak to a very important and relatively widely debated and ‘grand’ theory - the theory of extrinsic vs intrinsic motivation. Speaking to this larger issue, and larger debate makes the research feel important, and connected to a larger academic literature. And also notice they speak to a problem or issue which many people do care about - it is an everyday part of most people’s lives, but also something we don’t really talk about.

Tight budget: But also notice how they did the study quite simply and ‘cheaply’. They did an online survey, which was probably only 10 minutes in length. They also did qualitative interviews, which were basically just conversations with friends and other students at university, which they wrote up notes on (and got ethical permission from the interviewees to use).

As a researcher, I always fail at this challenge. But it is important to always keep in mind we want to speak to ‘big issues, with less work!’.

4. What is knowledge?

So we are doing research, but what is the purpose?

We know the purpose of research and study is ‘knowledge’. We want to ‘know stuff’.

But how exactly does our research generate knowledge? Where does it come from?

4.1 Evidence: Verifiable information

In universities, and in the scientific, academic, and social scientific community more generally, we are interested in generating knowledge which is based on evidence.

What is evidence? Well it is something that someone else can verify. It is something that exists outside any one of us as an individual. It is something someone else can confirm or check.

Knowledge which is not based on evidence includes things like:

Traditional knowledge

Opinions

Assumptions

Hunches

Guesses

It doesn’t mean these types of knowledge are wrong. And these ways of knowing can be tested, and they can be based on evidence which perhaps now doesn’t exist.

4.2 Theories: Generalised knowledge

It turns out, however, that academic knowledge, social scientific knowledge is not just evidence. It is not just a list of facts or statistics.

Social scientific knowledge aims at identifying patterns in the social world.

We call these patterns ‘theories’. We also called them ‘generalised knowledge’. Another word often thrown around to mean similar is ‘explanations’.

The reason social scientists have theories, and aim at generalised knowledge, is because we want to somehow understand and explain the world. We can’t do this if speak only about specific cases or examples.

So social scientists study, for example, the impact of gender on pay (rather than whether Julie is paid less than David).

Example of evidence and theories

So in the Hairrifying Truth" presentation we saw the theories of intrinsic and extrinsic motivation (whether you do something out of an internal drive, or whether you do it to please other people around you).

One piece of evidence the students collected was women’s answer to “Why do you trim your pubic hair?”

They collected these as open ended survey questions (i.e. women could just type out their reasons), and then the students classified these into ‘intrinsic’ motivations (such as cleanliness, health, convenience), and ‘extrinsic’ motivations (such as social pressure from boyfriends).

And they showed, in the first part of the study, that women tended to say they were motivated to trim pubic hair because of ‘intrinsic’ motivation - they did it for themselves.

This is a neat and simple example of testing a ‘theory’ (a piece of generalised knowledge) with evidence (since any of us could, if we wanted, reproduce that study, or we could even ask to check the surveys - if they still existed).

5. Focusing a Topic

So when we plan a study, we need to get the scope right - speaking to big issues, but with a small budget. And we do this by testing ‘theories’ (generalised knowledge) through collecting evidence. But this is all still very abstract. Can we pull apart this process and make it more systematic and clear?

Generally we talk about this process as ‘choosing a topic’ and ‘narrowing a topic’ or ‘focusing a topic’.

5.1 Research Question

One main tool we use to do this is to try to clearly articulate, in just one sentence, our research question.

There are many ways to frame research questions, and the textbook provides considerable discussion on types of research questions.

Good research questions are generally:

Simple: Able to be expressed in one sentence.

Testable: You can collect data to show them as right or wrong.

Not too vague or overly ambitious: We don’t want to blow the budget.

Speak to important issue.

Explanatory: They try to understand or test whether (or how) one thing gives rise to another (a cause gives rise to an effect).

Example of research question

In the case of the ‘Hairrifying Truth’, the students asked “Is women’s trimming of pubic hair more driven by intrinsic or extrinsic motives?”

5.2 Hypothesis

But how do we answer a research question?

There are a number of different approaches in the social sciences - some which are more like the physical sciences (geology, biology, chemistry); and some which are more like the humanities (history, literature, philosophy).

We tend to say that those social scientists who take a more humanities type approach tend to be ‘inductive’ in their approach developing theories and testing knowledge. They tend to start with the data, and from that building up explanations and theories and knowledge. In general (though not always), researchers from this perspective tend to prefer qualitative methods (such as interviews, focus groups, fieldwork).

In contrast, those social scientists who take a more natural science approach tend to be ‘deductive’ in their approach to developing and testing theories. They tend to start with a theory - or a competing set of theories - and then look at the real world to see which seems to be correct.

When researchers use this ‘deductive’ approach, they will tend to follow the familiar technique from the natural sciences of developing a ‘hypothesis’.

A hypothesis is a testable statement, generally involving two or more ‘variables’.

Variables are simply concepts that are measurable, and which can have different values.

Example of a variable

Gender can be a variable, which can take the values of male, female, or several other alternatives.

Age can be a variable

Support for Medicare can be a variable (perhaps they could have said “Yes” “No” or “No sure”)

Example of a hypothesis

If we had the research question:

How does gender and age effect support for Medicare in Australia?

We could develop a couple of hypothesises, such as:

That women are more likely to support Medicare.

That older people are more likely to support Medicare.

5.3 Independent and Dependent Variable (Cause and Effect)

Most theories, and therefore most hypotheses will make statements about cause and effect.

This is important and necessary for social scientists. We want to know why things happen, to to know why things happen we need to ask questions like “Does gender effect support for Medicare?”

Social scientists have fancy words for causes and effects, and it is important to know this jargon.

An independent variable: This is generally a ‘cause’. It is also called a predictor variable. It is a variable that is assumed to potentially generate a change in the ‘dependent’ variable (the outcome or effect).

A dependent variable: This is generally the ‘effect’ we are interest in. It is also called the ‘outcome’ variable. It is what we are interested in ‘explaining’.

Example of dependent and independent variables

So we can break down a topic like this:

Topic: Gender and Attitudes Towards Public Health Care in Australia

Research Question: How does gender affect support for Medicare in Australia?

Hypothesis: That women show higher levels of support for Medicare.

Independent variable: Gender

Dependent variable: Support for Medicare

6. Types of Data: Qualitative and Quantitative

To answer our research questions, and test our hypotheses, we need to collect data.

We tend to talk about two broad types of data in social science research:

Qualitative data: data that is words or images

Quantitative data: data that can be studied as numbers

When trying to remember which is which, remember:

Qualitative derives from the word ‘quality’ - things that differ in quality, not numbers, such as words and images

Quantiative derives from the world ‘quantity’ - things that differ in number, that can be counted.

Examples of qualitative data

In depth interviews

Focus groups

Fieldwork (i.e. spending time in natural settings, such as an office, or a village)

Photos

Documents, such as government archives.

Examples of quantitative data

surveys and censuses

experiments

observation, such as counting shoppers in a mall

running records, such as those of a government or company

7. Types of Analysis

Once we have collected data, we need to analyse it.

What does it mean to analyse data? It means to look at it closely.

Generally we analyse something by breaking it down into parts.

Often we also analyse data by trying to recombine the broken down parts into new patterns.

While there are many, many different ways to analyse data, I think at this stage it is helpful to think about two fundamental ways that we analyse qualitative and quantitative data.

7.1 Thematic analysis

For qualitative data, we tend to use thematic analysis. This means that we try to find common patterns - called themes - in the text or images. We might, for example, find that women tend to talk about five different reasons they trim pubic hair: cleanliness, convenience, boyfriend pressure, feels good, don’t know. These five ‘reasons’ are five ‘themes’ in a thematic analysis.

7.2 Statistical analysis

For quantitative data, we tend to use statistical analysis. This means we use a range of techniques to try to:

summarise the trends in the data

describe the variation in the data

show patterns and correlations between variables in the data

understand and describe other relationships in the data

Examples of thematic and statistical analysis

In the ‘Hairrifying Truth’ paper, students reported the number of women who used different trimming techniques (a summary). They also showed how some trimming techniques correlated with having sex with men (all but bikini). This was a pattern. And from this, they were able to draw conclusions.